We use the throughputs to evaluate the inference speed of neighbor retrieval of different methods.
The throughput is defined as how many source nodes that a method can serve to retrieve the top 100 scoring neighbors per second.
Except for the LP models that follow the encoder and decoder architectures, e.g., GraphSAGE [1]}, GCN [2]}, and PLNLP [3]}, there are some subgraph based LP models, e.g., SUREL [4]} and SEAL [5]}.
The common issue of the subgraph based models is the poor efficiency: they have to crop a seperate subgraph for every node pair to calculate the LP probability on the node pair.
In this sense, the node embeddings cannot be shared on the LP calculation for different node pairs.
This leads to the much lower inference speed of the subgraph based LP models than the encoder-decoder LP models.
We compare the inference effeciency of different methods on the OGBL-CITATION2 dataset in Fig. REF , where we present the inference speed of different methods when achieving the 100% recall\(@100\)  for the top 100 scoring neighbors.
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